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» Generalizing over Several Learning Settings
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ICML
2004
IEEE
14 years 1 months ago
Active learning of label ranking functions
The effort necessary to construct labeled sets of examples in a supervised learning scenario is often disregarded, though in many applications, it is a time-consuming and expensi...
Klaus Brinker
ICML
2006
IEEE
14 years 8 months ago
Agnostic active learning
We state and analyze the first active learning algorithm which works in the presence of arbitrary forms of noise. The algorithm, A2 (for Agnostic Active), relies only upon the ass...
Maria-Florina Balcan, Alina Beygelzimer, John Lang...
ICDE
2010
IEEE
194views Database» more  ICDE 2010»
14 years 7 months ago
XColor: Protecting General Proximity Privacy
As a severe threat in anonymized data publication, proximity breach is gaining increasing attention. Such breach occurs when an attacker learns with high confidence that the sensit...
Ting Wang, Ling Liu
DATAMINE
2006
166views more  DATAMINE 2006»
13 years 7 months ago
Accelerated EM-based clustering of large data sets
Motivated by the poor performance (linear complexity) of the EM algorithm in clustering large data sets, and inspired by the successful accelerated versions of related algorithms l...
Jakob J. Verbeek, Jan Nunnink, Nikos A. Vlassis
ICCV
2005
IEEE
14 years 9 months ago
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification m...
Kristen Grauman, Trevor Darrell